On the Probabilistic Approximation in Reproducing Kernel Hilbert Spaces

Fuente: arXiv
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Autori principali: Chen, Dongwei, Wang, Kai-Hsiang
Natura: Preprint
Pubblicazione: 2024
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author Chen, Dongwei
Wang, Kai-Hsiang
author_facet Chen, Dongwei
Wang, Kai-Hsiang
contents This paper studies the probabilistic function approximation problem over reproducing kernel Hilbert spaces. We show the existence and uniqueness of the optimizer under mild assumptions. Furthermore, we generalize the celebrated representer theorem to our setting, and especially when the probability measure is finitely supported, or the Hilbert space is finite-dimensional, we show that the probabilistic approximation problem turns out to be a measure quantization problem, which connects the probabilistic function approximation to the sampling theory. Some discussions and examples are also given when the reproducing kernel Hilbert space is infinite-dimensional and the measure is infinitely supported.
format Preprint
id arxiv_https___arxiv_org_abs_2409_11679
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle On the Probabilistic Approximation in Reproducing Kernel Hilbert Spaces
Chen, Dongwei
Wang, Kai-Hsiang
Functional Analysis
46E22
This paper studies the probabilistic function approximation problem over reproducing kernel Hilbert spaces. We show the existence and uniqueness of the optimizer under mild assumptions. Furthermore, we generalize the celebrated representer theorem to our setting, and especially when the probability measure is finitely supported, or the Hilbert space is finite-dimensional, we show that the probabilistic approximation problem turns out to be a measure quantization problem, which connects the probabilistic function approximation to the sampling theory. Some discussions and examples are also given when the reproducing kernel Hilbert space is infinite-dimensional and the measure is infinitely supported.
title On the Probabilistic Approximation in Reproducing Kernel Hilbert Spaces
topic Functional Analysis
46E22
url https://arxiv.org/abs/2409.11679